Nothing
library(hetGP)
context("IMSE")
test_that("IMSE",{
set.seed(1)
nvar <- 2
## Branin redefined in [0,1]^2
branin <- function(x){
if(is.null(nrow(x)))
x <- matrix(x, nrow = 1)
x1 <- x[,1] * 15 - 5
x2 <- x[,2] * 15
(x2 - 5/(4 * pi^2) * (x1^2) + 5/pi * x1 - 6)^2 + 10 * (1 - 1/(8 * pi)) * cos(x1) + 10
}
## Noise field via standard deviation
noiseFun <- function(x){
if(is.null(nrow(x)))
x <- matrix(x, nrow = 1)
return(1/20*(3*(2 + 2*sin(x[,1]*pi)*cos(x[,2]*3*pi) + 5*rowSums(x^2))))
}
## data generating function combining mean and noise fields
ftest <- function(x){
return(branin(x) + rnorm(nrow(x), mean = 0, sd = noiseFun(x)))
}
## Predictive locations for comparisons
ntest <- 1e5
Xgrid <- matrix(runif(ntest*2), ntest, 2)
## Unique (randomly chosen) design locations
n <- 20
Xu <- matrix(runif(n * 2), n)
## Select replication sites randomly
X <- Xu[sample(1:n, 10*n, replace = TRUE),]
## obtain training data response at design locations X
Z <- ftest(X)
## Formating of data for model creation (find replicated observations)
prdata <- find_reps(X, Z, rescale = FALSE, normalize = FALSE)
for(covtype in c("Gaussian", "Matern5_2", "Matern3_2")){
for(trend in c(NA, 0)){
if(is.na(trend)) trend <- NULL
## Model fitting
model <- mleHetGP(X = list(X0 = prdata$X0, Z0 = prdata$Z0, mult = prdata$mult), Z = prdata$Z,
lower = rep(0.01, nvar), upper = rep(1, nvar), known = list(beta0 = trend),
covtype = covtype)
IMSE <- IMSPE(model)
# Finite sum estimation:
preds <- predict(model, Xgrid)
IMSE_est <- mean(preds$sd2)
expect_equal(IMSE, IMSE_est, tol = 1e-2)
}
}
})
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.